PyGMTSAR (Python InSAR): Powerful, Accessible Satellite Interferometry
PyGMTSAR (Python InSAR) is designed for both occasional users and experts working with Sentinel-1 satellite interferometry. It supports a wide range of features, including SBAS, PSI, PSI-SBAS, and more. In addition to the examples below, you’ll find more Jupyter notebook use cases on Patreon and updates on LinkedIn.
About PyGMTSAR
PyGMTSAR offers reproducible, high-performance Sentinel-1 interferometry accessible to everyone—whether you prefer Google Colab, cloud servers, or local processing. It automatically retrieves Sentinel-1 SLC scenes and bursts, DEMs, and orbits; computes interferograms and correlations; performs time-series analysis; and provides 3D visualization. This single library enables users to build a fully integrated InSAR project with minimal hassle. Whether you need a single interferogram or a multi-year analysis involving thousands of datasets, PyGMTSAR can handle the task efficiently, even on standard commodity hardware.
PyGMTSAR Live Examples on Google Colab
Google Colab is a free service that lets you run interactive notebooks directly in your browser—no powerful computer, extensive disk space, or special installations needed. You can even do InSAR processing from a smartphone. These notebooks automate every step: installing PyGMTSAR library and its dependencies on a Colab host (Ubuntu 22, Python 3.10), downloading Sentinel-1 SLCs, orbit files, SRTM DEM data (automatically converted to ellipsoidal heights via EGM96), land mask data, and then performing complete interferometry with final mapping. You can also modify scene or bursts names to analyze your own area of interest, and each notebook includes instant interactive 3D maps.
The resulting InSAR velocity map is available as a self-contained web page at: Imperial_Valley_2015.html
More PyGMTSAR Live Examples on Google Colab
PyGMTSAR Live Examples on Google Colab Pro
I share additional InSAR projects on Google Colab Pro through my Patreon page. These are ideal for InSAR learners, researchers, and industry professionals tackling challenging projects with large areas, big stacks of interferograms, low-coherence regions, or significant atmospheric delays. You can run these privately shared notebooks online with Colab Pro or locally/on remote servers.
Projects and Publications Using PyGMTSAR
See the Projects and Publications page for real-world projects and academic research applying PyGMTSAR. This is not an exhaustive list—contact me if you’d like your project or publication included.
Resources
PyGMTSAR projects and e-books Available on Patreon. Preview versions can be found in this GitHub repo:
Video Lessons and Notebooks Find PyGMTSAR (Python InSAR) video lessons and educational notebooks on Patreon and YouTube.
PyGMTSAR AI Assistant The PyGMTSAR AI Assistant, powered by OpenAI ChatGPT, can explain InSAR theory, guide you through examples, help build an InSAR processing pipeline, and troubleshoot.
PyGMTSAR on DockerHub Run InSAR processing on macOS, Linux, or Windows via Docker images.
PyGMTSAR on PyPI Install the library from PyPI.
PyGMTSAR Previous Versions 2023 releases are still on GitHub, PyPI, DockerHub, and Google Colab. Compare PyGMTSAR InSAR with other software by checking out the PyGMTSAR 2023 Repository.
© Alexey Pechnikov, 2025
1 maintainer
Metadata
1 maintainer
Release files for pygmtsar 2025.4.8.post1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| pygmtsar-2025.4.8.post1.tar.gz | 10.8 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| pygmtsar-2025.4.8.post1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 21.6 MB
Release files / pygmtsar-2025.4.8.post1.tar.gz
| Download URL | pygmtsar-2025.4.8.post1.tar.gz |
|---|---|
| Size | 10.8 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
cf946afed2c03cd70ab1442ffa4ebd0dc41dcc3e82b311b9ee493476c39961a1
|
|
BLAKE2b-256 checksum How to use checksums |
bf51922a6b0e13481c459f8fd1fa58b0836d8f85c470508ff0b2baf8fe864f5f
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|
Release files / pygmtsar-2025.4.8.post1-py3-none-any.whl
| Download URL | pygmtsar-2025.4.8.post1-py3-none-any.whl |
|---|---|
| Size | 10.8 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
e42bb0bbfec06acce01ee2cdbda9e5dd7e0583245499af07a1a0df6a46780e16
|
|
BLAKE2b-256 checksum How to use checksums |
b53923bdae6cfea7a96037dd17c89f1ed53fd913893bd609dad7358831db3636
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.1.0 CPython/3.13.2
|
